A Comparison of Bayesian Methods and Artificial Neural Networks for Forecasting Chaotic Financial Time Series

2012 ◽  
Vol 1 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Tamer Shahwan ◽  
Raed Said
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Bayesian Methods ◽  
Financial Time Series ◽  
Financial Time ◽  
Artificial Neural

MEMORY IN FINANCIAL TIME SERIES: FROM INVESTOR BEHAVIOR TO ARTIFICIAL NEURAL NETWORKS

2012 ◽  
Author(s):  
M. TERESA SORROSAL-FORRADELLAS ◽  
M. GLÒRIA BARBERÀ-MARINÉ ◽  
LISANA B. MARTINEZ ◽  
MARÍA-JOSÉ GARBAJOSA-CABELLO
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Financial Time Series ◽  
Investor Behavior ◽  
Financial Time ◽  
Artificial Neural

scholarly journals Financial time series forecasting using Artificial Neural Networks

2019 ◽  
Vol 15 (1) ◽  
pp. 105-122
Author(s):  
Roberto Gallardo Del Ángel
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Financial Time Series ◽  
Time Series Forecasting ◽  
Financial Time ◽  
Artificial Neural

Este documento contiene una predicción financiera utilizando Redes Neuronales Artificiales. Hacemos nuestro análisis utilizando el algoritmo de Backpropagation tradicional y luego Backpropagation Resiliente para estimar los pesos en las redes. El uso del algorithm de Bacpropagation Resiliente permite resolver el problema de la determinación de la tasa de aprendizaje. Ambos algoritmos son bastante consistentes y arrojan predicciones similares. Analizamos seis índices principales de los mercados bursátiles de Europa, Asia y América del Norte para generar índices de aciertos que puedan compararse entre mercados. Usamos precios de cierre diarios para construir una variable de dependiente para dirigir el aprendizaje (aprendizaje supervisado) y una matriz de variables de características construidas utilizando indicadores de análisis técnico. El rango de datos de la serie de tiempo va desde Enero de 2000 a Junio de 2019, un periodo de grandes fluctuaciones debido a mejoras en la tecnología de la información y una alta movilidad de capital. En lugar de la predicción en sí misma, el objetivo científico es evaluar la importancia relativa de las variables independientes que permiten la predicción. Utilizamos dos medidas de contribución utilizadas en la literatura para evaluar la relevancia de cada variable para los seis mercados financieros analizados. Descubrimos que estas medidas no siempre son consistentes, por lo que construimos una medida de contribución simple que le da a cada peso una interpretación geométrica. Proporcionamos algunas pruebas de que la tasa de cambio (ROC) es la herramienta de predicción más útil para cuatro índices generales, con las excepciones siendo el índice Hang Sheng y EU50, en donde el fastK es el más destacado.

scholarly journals Modeling and Forecasting Cryptocurrency Closing Prices with Rao Algorithm-Based Artificial Neural Networks: A Machine Learning Approach

FinTech ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 47-62
Author(s):  
Sanjib Kumar Nayak ◽  
Sarat Chandra Nayak ◽  
Subhranginee Das
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Financial Time Series ◽  
Optimal Parameters ◽  
Financial Time ◽  
Good Investment ◽  
Machine Learning Approach ◽  
Modeling And Forecasting ◽  
The Mean ◽  
Artificial Neural

Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.

Design of an Artificial Neural Network battery for an optimal recognition of patterns in financial time series

2018 ◽  
Vol 05 (04) ◽  
pp. 1850031
Author(s):  
Simone Fioribello ◽  
Pier Giuseppe Giribone
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Financial Time Series ◽  
Classification Problem ◽  
Automatic Recognition ◽  
Convolutional Network ◽  
The Third ◽  
Financial Time ◽  
Analysis Application ◽  
Artificial Neural

The increasingly massive use of advanced Machine Learning methodologies in the financial field sector has led credit institutions to quickly move to new FinTech technologies. This paper deals with how a battery of Artificial Neural Networks (ANN), dedicated to the automatic recognition of financial patterns of potential interest to traders, can be designed and validated. The battery of neural networks that have been designed is composed of a shallow ANN, a deep ANN with ReLu, a deep ANN with Dropout and a convolutional network (ConvNet). Depending on the type of classification problem, the ANN battery dynamically recognizes the best classifier and makes use of it for pattern recognition. The first part of the paper describes how these technologies work, the second one performs a validation of the code and the third one suggests a technical analysis application on financial time series.

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